The near-real-time precipitation dataset developed by the Center for Hydrometeorology and Remote Sensing (CHRS) of the University of California, Irvine, has been updated and improved, as documented in a recent article in the Journal of Meteorology.
Developed and maintained with support from UNESCO G-WADI and others, the original Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks– Cloud Classification System (PERSIANN-CCS) dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (delay) of 15–60 minutes.
The new product, PERSIANN Dynamic Infrared Rain Rate (PDIR-Now), is intended to supersede PERSIANN–CCS as the primary near-real-time, quasi-global satellite precipitation dataset of the PERSIANN family.
An evaluation done during 2017–18 showed that PDIR-Now gave improved results over PERSIANN-CCS at all temporal scales, including the estimation of rain/no-rain days, seasonal and diurnal cycles of precipitation, and regional precipitation patterns.
PDIR-Now has already been incorporated into the iRain interface (https://irain.eng.uci.edu/), a website that provides a user-friendly interface to visualize global precipitation dataset for the last 72 hours, and can be downloaded from the CHRS Data Portal (https://chrsdata.eng.uci.edu/), which is an interface for the download of PDIR-Now dataset as well as other PERSIANN family datasets.